1. Technical Field
The present disclosure relates generally to the field of imaging, and more particularly to reconstruction of medical images using prior knowledge associated with transform domain coefficients.
2. Discussion of Related Art
Compressed Sensing (CS) theory provides an attractive alternative to conventional Nyquist sampling. CS theory demonstrated that, under certain conditions, signals with sparse representations can be reconstructed from much fewer measurements than suggested by the Nyquist theory. Such reconstruction can be quite useful in applications where acquiring data at the desired Nyquist rate is either expensive or impossible due to physical/hardware constraints.
Signals of practical interest have sparse representations using a transform domain. Sparsity refers the number of nonzero components when most transform coefficients except a few large coefficients are approximated to zero. Based on a sparseness assumption, CS theory states that a signal can be reconstructed by finding the sparsest solution among infinitely many candidates satisfying the measurements. Existing CS reconstruction techniques include reweighted L1 minimization (RL1), Iteratively Reweighted Least Squares (IRLS), and Iterative Hard Thresholding (IHT). However, these techniques do not exploit prior knowledge about the statistical dependencies between transform domain coefficients beyond sparsity.